Radial Basis Function (RBF) Network comprises an input vector followed by a layer of RBF neurons and an output layer with one node per class. Classification is accomplished by assessing the input’s resemblance to data points from the training set where each neuron stores a prototype, which is one of the samples from the training set. When a novel input vector (the n-dimensional vector that you are trying to classify) needs to be classified, every neuron computes the Euclidean distance between the input and its prototype [1]}, [2]}, [3]}. For example, if we have two classes, i.e., class A and Class B, then if the new input is much closer to class A, it will be labeled as class A. Each RBF neuron associates the input vector to its prototype and outputs a value alternating, a measure of resemblance from 0 to 1. As the input equals the prototype, the output of that RBF neuron will be 1, and with the distance produced between the input and prototype, the reply falls off exponentially towards 0. The curve produced by the neuron’s response inclines to a typical bell curve [4]}, [5]}. Fig. REF  shows the simple architecture of RBF comprising of an input layer, a hidden layer (consisting of several RBF non-linear activation units), and an output layer.
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